We are in the midst of a worldwide epidemic of diabetes and obesity. A central component of these disorders is insulin resistance. Insulin resistance is the product of gene-environment interactions. A recently identified major mediator of these gene-environment interactions is the gut microbiome. To begin to dissect the role of the microbiome in gene-environment interactions in the pathogenesis of type 2 diabetes and obesity, we have developed a novel model taking advantage of three strains of laboratory mice: C57Bl6/J and 129S1 mice from Jax (B6J and 129J) and 129S6 mice from Taconic (129T). When challenged with high fat diet (HFD), B6J mice are insulin resistant and obesity- and diabetes-prone, while 129J mice are insulin sensitive and obesity- and diabetes-resistant. 129T mice, which are similar genetically to 129J, on the other hand, gain almost as much weight as B6J mice on HFD, but remain insulin sensitive and non-diabetic, i.e., are a model of ?metabolically healthy? obesity. While genetics plays a role in these phenotypic differences, the microbiome also contributes. Thus, some of these differences can be reduced or modified by breeding the mice in the same environment or by treating the mice with antibiotics to alter the microbiome. These differences in phenotype are paralleled by differences in insulin signaling at the molecular level. Importantly, the propensity to metabolic syndrome and abnormalities in insulin signaling can be transferred in part to germ-free mice by fecal transplant. Using non-targeted metabolomics, we have shown that these effects of the microbiome are associated with dramatic changes in the levels of multiple circulating metabolites, including both known and unknowns. The major goal of this project is to identify microbiota and metabolites which are altered by the changing microbiome and contribute to insulin resistance and metabolic dysregulation. The specific aims are: 1) Using our robust model of mice on three different genetic backgrounds, we will define how changes in gut microbiota, as assessed by metagenomic analysis, in response to high fat and high carbohydrate diets, as well as exercise, are related to alterations in insulin signaling and metabolic phenotype; we will also determine how host-genetics interacts with gut microbiota to affect the metabolome by microbiome transfer into mice with different genetic risk of diabetes and metabolic syndrome. 2) Define how changes in the community of microbiota and their metagenomic representation relate to changes in the plasma/cecal metabolome across all models, and how these contribute to the insulin resistance in these models. We will also integrate the metabolomics data to create complete metabolic networks. 3) Integrate metabolomic data across all models to prioritize the unknown metabolites linked to insulin resistance for identification; and determine how both the known and the newly-identified unknown metabolites linked to insulin resistance alter insulin signaling in vitro and in vivo. Together these data will allow us to define the role of the microbiome and its associated metabolome in insulin resistance and metabolic dysregulation and how these interact with host genetics in this process.